Katherine Rasmussen

The agile software development practice of test-driven development (TDD) advocates unit testing as an essential driver of software design and construction. In TDD, tests of individual units of software (e.g., procedures) serve documentation and verification roles. As documentation, tests specify the behaviors required for code correctness. Executing a suite of tests verifies that the actual behaviors satisfy the documented requirements. As inspired by the Veggies and Garden unit testing frameworks for modern Fortran, the more lightweight Julienne framework uses the Template Method pattern to report serial or parallel test results in the form of a specification (https://go.lbl.gov/julienne). As such, Julienne’s test output names the test subject (e.g., a class or type-bound procedure), the expected behavior, the test outcome (pass or fail), and provides diagnostic information if a test fails.

The use of Julienne centers around users defining a test in the form of a non-abstract child type that extends Julienne’s abstract test_t derived type. The user’s child type thus inherits an obligation to define type-bound procedures that name the subject of the test and provide the test results. As a template method, test_t’s type-bound “report” procedure invokes the user’s procedures by referencing the aforementioned deferred bindings and reporting on the collective success or failure across multiple images (processes) in programs that use Fortran’s multi-image parallel programming features.

Working from the example test suite in the Julienne repository, attendees will learn how to write and run a simple test suite, including how to use Julienne’s string-handling for producing rich diagnostic information from a failing test. Attendees will also see examples of Julienne’s use in other Berkeley Lab software projects such as the Fiats deep learning library and Matcha T-cell motility simulator.

Attendees will also learn a functional programming pattern developed and used by the Berkeley Lab Fortran presenters. Functional programming centers around the definition of pure procedures that are free of side effects, including file input and output. To supplement the material on external verification via unit tests, this tutorial will also introduce our Assert utility library and Assert’s use for runtime correctness-checking inside procedures (https://go.lbl.gov/assert). Attendees will learn how Assert addresses a common reason developers cite for not writing pure procedures: a desire to produce diagnostic output when debugging code. We posit that most developers seek output to verify an expectation about data and that such expectations can be stated in assertions that take the form of logical expressions. Attendees will learn how Assert empowers developers to obtain rich, customized diagnostic information through character stop codes when an assertion fails, resulting in error termination. Attendees will also learn how to use Assert in such a way that guarantees zero runtime overhead by automatically eliminating assertions in production builds of user software.

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